1. Tracking Changes Across ML Pipelines with AWS Config.


    To track changes across ML pipelines using AWS Config, we can define AWS Config rules that evaluate the configuration settings of AWS resources within your environment. These rules can help ensure compliance with your desired configuration settings. Since AWS Config continuously monitors and records your AWS resource configurations, you can use it to audit changes in your ML pipeline infrastructure, identify non-compliant resources, and, if necessary, implement remediation actions.

    Here's a high-level overview of the steps we'll take to set up this tracking:

    1. AWS Config Recorder: First, we'll set up a configuration recorder to track resource changes and capture historical configurations.
    2. AWS Config Rule: Next, we'll create Config rules that define the desired configurations for specific resource types involved in the ML pipeline, like EC2 instances or S3 buckets.
    3. AWS Config Delivery Channel: We'll establish a delivery channel to specify where AWS Config delivers the configuration snapshots and changes identified.
    4. Tagging and Scope: We'll apply tags to resources within our ML pipelines to help scope the Config rules to the relevant resources only.
    5. Remediation Actions: Optionally, we can define remediation actions that AWS Config should take when a resource is found to be non-compliant.

    Below is a Pulumi Python program that sets up these AWS Config components:

    import pulumi import pulumi_aws as aws # Create an IAM role that AWS Config will assume config_role = aws.iam.Role("config-role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": ["config.amazonaws.com"] }, "Action": "sts:AssumeRole" }] }""" ) # Attach managed policy to the IAM role for AWS Config service role permissions role_policy_attachment = aws.iam.RolePolicyAttachment("role-policy-attachment", role=config_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSConfigRole" ) # Create an S3 bucket for storing configuration history and snapshot data config_bucket = aws.s3.Bucket("config-bucket") # Create the AWS Config recorder to record configuration changes config_recorder = aws.cfg.Recorder("config-recorder", role_arn=config_role.arn, recording_group=aws.cfg.RecorderRecordingGroupArgs( all_supported=True, include_global_resource_types=True, ) ) # Create an AWS Config rule to evaluate the desired configuration config_rule = aws.cfg.Rule("config-rule", source=aws.cfg.RuleSourceArgs( owner="AWS", source_identifier="S3_BUCKET_VERSIONING_ENABLED", # Example managed rule for S3 bucket versioning ), # Define scope to restrict the rule to apply to specific resource types or tags scope=aws.cfg.RuleScopeArgs( # Optionally fill in `tag_key` and `tag_value`, # or `compliance_resource_id` to specify resource scope ), input_parameters="""{ "desired_value":"true" # Define parameters for the rule }""" ) # Create the AWS Config delivery channel to specify how AWS Config delivers the configuration snapshots delivery_channel = aws.cfg.DeliveryChannel("delivery-channel", s3_bucket_name=config_bucket.id, # Optionally include an S3 key prefix, SNS topic ARN, and snapshot delivery properties ) # Export the S3 bucket name and ARN of the IAM role for AWS Config pulumi.export('config_bucket_name', config_bucket.id) pulumi.export('config_role_arn', config_role.arn)

    In this program, we create each of the components necessary to set up AWS Config for your environment, which you can use to track changes to ML pipeline resources. We have defined a simple AWS Config rule for illustration, but you'll need to define rules based on your own ML pipeline's resources and desired configuration settings.

    Here's a brief description of each created resource:

    • IAM Role for AWS Config: We create an IAM role which AWS Config will assume to access your AWS resources.

    • S3 Bucket for Configuration History: We set up an S3 bucket to store the configuration history and snapshot data that AWS Config records.

    • Config Recorder: The recorder captures all changes to the resource configurations within its scope.

    • Config Rule: The rule specifies the configurations to check for (in our example, we used S3 bucket versioning as a commonly desired configuration).

    • Delivery Channel: The channel defines how recorded configurations and snapshots are delivered to the specified S3 bucket.

    Please ensure that you have the necessary permissions to create these AWS resources and that your Pulumi program has the appropriate AWS credentials configured.

    Keep in mind that additional steps may be necessary to completely set up AWS Config for your environment, such as setting up more specific AWS Config rules tailored to your ML infrastructure, defining tags for scoping, or implementing automated remediation actions.